Meta-Learning for Wireless Communications: A Survey and a Comparison to GNNs
Deep learning has been used for optimizing a multitude of wireless problems. Yet most
existing works assume that training and test samples are drawn from the same distribution …
existing works assume that training and test samples are drawn from the same distribution …
Automatic High-Performance Neural Network Construction for Channel Estimation in IRS-Aided Communications
Accurate channel estimation is an essential prerequisite for achieving significant
performance gains in intelligent reflecting surface (IRS)-aided communication systems …
performance gains in intelligent reflecting surface (IRS)-aided communication systems …
Deep Reciprocity Calibration for TDD mmWave Massive MIMO Systems Towards 6G
Ideally, the bi-directional channel in time division duplex (TDD) millimeter wave (mmWave)
massive multiple-input multiple-output (MIMO) systems exhibits reciprocity. However, the …
massive multiple-input multiple-output (MIMO) systems exhibits reciprocity. However, the …
Learning-to-Learn the Wave Angle Estimation
A precise incident wave angle estimation in aerial communication is a key enabler in sixth-
generation wireless communication network. With this goal, a generic 3-dimensional (3D) …
generation wireless communication network. With this goal, a generic 3-dimensional (3D) …
Learning Precoding Policy with Inductive Biases: Graph Neural Networks or Meta-Learning?
Deep learning has been introduced to optimize wireless policies such as precoding for
enabling real-time implementation. Yet prevalent studies assume that training and test …
enabling real-time implementation. Yet prevalent studies assume that training and test …
Entropy-based Probing Beam Selection and Beam Prediction via Deep Learning
Hierarchical beam search in mmWave communications incurs substantial training overhead,
necessitating deep learning-enabled beam predictions to effectively leverage channel priors …
necessitating deep learning-enabled beam predictions to effectively leverage channel priors …
[HTML][HTML] Knowledge graph learning algorithm based on deep convolutional networks
Y Zhou, Z Lin, J Lin, Y Yang, J Shi - Intelligent Systems with Applications, 2024 - Elsevier
Abstract Knowledge graphs (KGs) serve as invaluable tools for organizing and representing
structural information, enabling powerful data analysis and retrieval. In this paper, we …
structural information, enabling powerful data analysis and retrieval. In this paper, we …
[HTML][HTML] Wireless federated learning for PR identification and analysis based on generalized information
J Liu, Y Li, J Zhou, H Hua, P Zhang - Intelligent Systems with Applications, 2024 - Elsevier
This paper introduces a novel approach to personal risk (PR) identification using federated
learning (FL) in wireless communication scenarios, leveraging generalized information. The …
learning (FL) in wireless communication scenarios, leveraging generalized information. The …
Energy Efficient Beamforming Design for Non-Orthogonal Multiple Access Systems: A Curiosity-Driven Approach
In this work, beamforming design and resource allocation for overload non-orthogonal
multiple access (NOMA) systems is investigated. Based on this, a pure-NOMA framework is …
multiple access (NOMA) systems is investigated. Based on this, a pure-NOMA framework is …
Compression and Transmission of Big AI Model Based on Deep Learning: Compression and Transmission of Big AI Model Based on Deep Learning
Z Lin, Y Zhou, Y Yang, J Shi, J Lin - EAI Endorsed Transactions …, 2024 - publications.eai.eu
In recent years, big AI models have demonstrated remarkable performance in various
artificial intelligence (AI) tasks. However, their widespread use has introduced significant …
artificial intelligence (AI) tasks. However, their widespread use has introduced significant …